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2.
• Academic – working on social media and Big Data (methods)
• Have been studying social media for nearly a decade
• Sometime data journalist for The Guardian
• Co-author of the Data Journalism Handbook
• Led social media analysis of ‘Reading the Riots on Twitter’ (2.5M)
• Fellow recipient of inaugural Data Journalism Award (for Twitter
rumour visualization as part of Reading the Riots)
• Now: verification – spread on (mis)information online
• Key concern for the World Economic Forum (top ten trend for 2014)
• World Economic Forum Global Agenda Council for Social Media
• Border runner – talk/work across academia | government | industry
• Advising various research councils on funding social media research
• New projects: Picturing the Social and Visual Social Media Lab
• MA module (option): Researching Social Media/ Research Methods
for Social Media (40 students 2012/2013; 65 students 2013/2014)

3.
"Picturing the Social: transforming our
understanding of images in social media and
Big Data research.”
ESRC Transformative Research grant

9.
FREE ONE DAY CONFERENCE
Picturing the Social:
Analysing Social Media Images
Part of ESRC Festival of Social Science
7 November: ICOSS, University of Sheffield
Registration opens 1 October
visualsocialmedialab.org

13.
Academic definition
• Technology: maximizing computation power and algorithmic
accuracy to gather, analyze, link, and compare large data
sets.
• Analysis: drawing on large data sets to identify patterns in
order to make economic, social, technical, and legal claims.
• Mythology: the widespread belief that large data sets offer a
higher form of intelligence and knowledge that can generate
insights that were previously impossible, with the aura of truth,
objectivity, and accuracy.
(boyd and Crawford p. 663).

14.
Critiques of Big Data
• Important to make visible inherent claims about objectivity
• Problematic focus on quantitative methods
• How can data answer questions it was not designed to
answer?
• How can the right questions be asked?
• Inherent biases in large linked error prone datasets
• Focus on text and numbers that can be mined algorithmically
• Data fundamentalism (correlation always indicates causation)

15.
How do we ground online data?
In the offline: assessing findings against what we know about
an offline population (census data) in order to better understand
online data. Problems with over/under representation in online
data?
In the online: premised on the idea that data derived from the
Internet should be grounded in other online data in order to
understand it. So comparing Facebook use to what we know
about Facebook use, rather than connecting it to offline
measurements about citizens.
Pioneered by Richard Rogers: online is the baseline.

16.
Important considerations for online research
1. Asking the right question – research should be question
driven rather than data or tool driven.
2. Accept poor data quality & users gaming metrics – once
online metrics have value users will try to game them.
3. Limitations of tools (often built in disconnected way)
4. Transparency – researchers should be upfront about
limitations of research and research design. Can the data
answer the questions?

17.
Organic data / data in the wild
SOCIAL MEDIA SIMPLY AS (BIG) DATA
VS
SOCIAL MEDIA AS A RESEARCH AREA

23.
Social media VS social data
• Social Media: User-generated content where one user
communicates and expresses themselves and that content is
delivered to other users. Examples of this are platforms such
as Twitter, Facebook, YouTube, Tumblr and Disqus. Social
media is delivered in a great user experience, and is focused
on sharing and content discovery. Social media also offers
both public and private experiences with the ability to share
messages privately.
• Social Data: Expresses social media in a computer-readable
format (e.g. JSON) and shares metadata about the content to
help provide not only content, but context. Metadata often
includes information about location, engagement and links
shared. Unlike social media, social data is focused strictly on
publicly shared experiences. (Cairns, 2013)

28.
Geo-locating tweets
Exact location
Lat/long coordinates
Gold standard geo data
Problem: only 1% of users
-> Only 2% of firehose tweets
Early adopters, highly skewed
Where in the world are you?
No Lat/long coordinates
Text field – enter anything
Advantage: more than half of all
tweets contain profile location
Much more evenly distributed

29.
Profile geo-enrichment
‘our customers can now hear from the whole world of Twitter
users and not just 1%’ (Cairs, 2013 on Gnip company Blog)
• Activity Location – 1% that provide lat/long
• Profile Location – Place provided in their profile. May or may
not be posting from there.
• Mentioned Location – Places a user talks about

30.
Profile geo-enrichment + linking data
‘Profile location data can be used to unlock demographic data
and other information that is not otherwise possible with activity
location. For instance, US Census Bureau statistics are
aggregated at the locality level and can provide basic stats like
household income. Profile location is also a strong indicator of
activity location when one isn’t provided. (Cairns, 2013)

31.
Dangers of mapping/tracking/predicting
Did it really work?
Beware of the fall of Google flu trends!
Results are rarely independently verified
or triangulated (can’t share social media data).

32.
Data collection problems
- Searching for keywords/tags: problematic. What are the
search terms you need/know about? Significant disadvantage
in searching for a limited number of hashtags: (1) not all
tweets pertaining to the relevant topic will contain a hashtag;
(2) how to identify all the relevant keywords/hashtags?
- Identifying users: problematic. How do you identify them?
Through information in their bio or additional registration
information accessible through the API (YouTube)? Self-declared
information.
- Searching for users/topics/keywords through networks
(Facebook, YouTube, Twitter). What does the network show?
What kind of connections?

33.
Social Network Analysis
Not new, applied to social media. Many sites structure information
through social graphs and connections between users/tags/keywords.
Could identify new information, important view on the data.
Limits: (1) What is the nature of the connection? What does a ‘friend’
connection mean? Could be friend/relative/ acquaintance/fan/
stranger/fake -> requires critical reflection, qualitative inspections.
(2) Difficult to account for changes over time. The network
visualisation only gives a snapshot in time. Not clear what that shows.
(3) How is the network graph constructed by the social media
company? How is the Facebook graph constructed? How are nodes
connected? Which ‘friends’ are shown? Which are left out?
(4) How does the visualisation tool (Gephi) show the network?

34.
Sentiment analysis
Developed for short texts (tweets, comments on YouTube, short
Facebook updates, comments) to establish positive and negative
sentiment. Great interest in sentiment analysis from both industry,
policy makers and academia (‘mapping the mood of the nation’ type
projects; identifying negative sentiment as an early warning sign). A
range of tools have been developed in this area, which allow for the
rapid processing of short structured texts.
Limits: (1) The stated text must include a measurable sentiment (a lot
of tweets and short texts are simply statements).
(2) The danger of reading out of context. Texts must be placed back
into their original context to make sense. Often impossible to do.
(3) Establishing sentiment is difficult for both human coder & machine

35.
Hashtag analysis
Hashtags originally emerged on Twitter, but now also widely used on
Facebook. By clicking them other content tagged in this way is
revealed. This data can be collected through APIs to gaining insight
into topics/events through their hashtag and the communities that can
be identified around them. These can arise quickly (breaking news);
establish around a popular TV show (#masterchef); a set of interests
(#foodporn); part of wider social media cultures (#ff #fail #facepalm).
Limits: (1) Sampling concerns around how Twitter makes data
available through API structure. Not clear how collected sample relates
to the firehose. Concern for social science researchers.
(2) How do you know you have ‘all’ the hashtags? Only partial data.

36.
Ethics
Ethics acknowledged as an important and hotly debated area of
social media research. Updated ethical guidelines from the
Association of Internet Researchers (AoIR) published last year.
Slow to update. Do we need more flexible frameworks and
frequent sharing of good practice? Is it feasible to seek informed
consent? Often seen as context specific.
Is the data public if it is publicly available? Does the notion of
‘public’ change when user registration data (for example age,
gender) can only be accessed with the API, but is not publicly
visible.

37.
Ethics
Most social media data is created within specific context.
Emerging approaches/best practice.
Quantitative: data shown in aggregate, not at individual level.
Users not identifiable.
Qualitative: Consent is sought, no data shared that can be
linked back to a user. ‘Agile ethics’: do to users what you feel
comfortable with in use of your own data. Problem with difficult to
identify underage users, most: from 13 years+